Viktor Todorov
Viktor Todorov

Harold H. Hines Jr. Professor of Risk Management
Co-chair of Faculty Research

Print Overview

Viktor Todorov is a Professor of Finance. He joined Kellogg in 2007 after completing his PhD in Economics at Duke University.

Professor Todorov's research interests include theoretical and empirical asset pricing, derivatives and econometrics. His recent research focuses on robust estimation of asset pricing models using high-frequency financial data as well as the identification and modeling of jump risk premium combining information from options markets.

Print Vita
PhD, 2007, Economics, Duke University
MA, 2002, Economics, Central European University
BA, 1999, Finance, Varna University of Economics

Academic Positions
Professor of Finance, Kellogg School of Management, Northwestern University, 2015-present
Associate Professor of Finance, Kellogg School of Management, Northwestern University, 2011-2015
Assistant Professor of Finance, Kellogg School of Management, Northwestern University, 2007-2011

Honors and Awards
Finalist for the AQR Insight Award for the paper ``The Risk Premia Embedded in Index Options'', AQR; Greenwich, CT
Elected Fellow, Journal of Econometrics
Elected Felllow, Society for Financial Econometrics
Arnold Zellner Thesis for best Thesis in Business and Economic Statistics , American Statistical Association

Editorial Positions
Editor, Econometric Theory, 2017-2020
Associate Editor, Econometrica, 2016-2019
Associate Editor, Journal of Financial Econometrics, 2012-2016
Associate Editor, Journal of Econometrics, 2012-2018
Associate Editor, Econometric Theory, 2014-2017

Print Research
Research Interests
Asset pricing, econometrics, applied probability

,  and . 2017. Short-Term Market Risks Implied by Weekly Options. Journal of Finance. 72(3): 1335-1385.
. Forthcoming. Testing for Time-Varying Jump Activity for Pure Jump Semimartingales. Annals of Statistics.
,  and . Forthcoming. Robust Jump Regressions. Journal of the American Statistical Association.
, ,  and . Forthcoming. Mixed-scale Jump Regressions with Bootstrap Inference. Journal of Econometrics.
,  and . 2016. Roughing up Beta: Continuous vs. Discontinuous Betas, and the Cross Section of Expected Stock Returns. Journal of Financial Economics. 120: 464-490.
,  and . 2017. Jump Regressions. Econometrica. 85: 173-195.
,  and . Forthcoming. Adaptive Estimation of Continuous-Time Regression Models using High-Frequency Data. Journal of Econometrics.
. 2015. Jump Activity Estimation for Pure-Jump Ito Semimartingales via Self-Normalised Statistics. Annals of Statistics. 43: 1831-1864.
,  and . 2015. Tail Risk Premia and Return Predictability. Journal of Financial Economics. 118: 113-134.
,  and . 2015. The Risk Premia Embedded in Index Options. Journal of Financial Economics. 117(3): 558-584.
,  and . 2016. Estimating the Volatility Occupation Time via Regularized Laplace Inversion. Econometric Theory. 32: 1253-1288.
,  and . 2016. Inference Theory for Volatility Functional Dependencies. Journal of Econometrics. 193: 17-34.
,  and . 2015. Parametric Inference and Dynamic State Recovery from Option Panels. Econometrica. 83(3): 1081-1145.
,  and . 2015. Nonparametric Test for a Constant Beta between Ito Semimartingales based on High Frequency Data. Stochastic Processes and Their Applications. 2955-2988: 2955-2988.
, ,  and . 2015. The Fine Structure of Equity-Index Option Dynamics. Journal of Econometrics. 187(2): 532-546.
and . 2014. Time-Varying Jump Tails. Journal of Econometrics. 183: 168-180.
and . 2014. Limit Theorems for the Empirical Distribution Function of Scaled Increments of Ito Semimartingales at High Frequencies. Annals of Applied Probability. 24: 1850-1888.
and . 2014. Efficient Estimation of Integrated Volatility in Presence of Infinite Variation Jumps. Annals of Statistics. 42: 1029-1069.
,  and . 2014. Volatility Activity: Specification and Estimation. Journal of Econometrics. 178: 180-193.
,  and . 2014. Volatility Occupation Times. Annals of Statistics. 41: 1865-1891.
. 2013. Realized Power Variation from Second Order Differences for Pure Jump Semimartingales. Stochastic Processes and Their Applications. 123: 2829-2850.
,  and . 2013. Jump Tails, Extreme Dependencies and the Distribution of Stock Returns. Journal of Econometrics.(172): 307-324.
,  and . 2013. Central Limit Theorems for Approximate Quadratic Variations of Pure Jump into Semimartingales. Stochastic Processes and Their Applicationa. 123: 839-886.
and . 2012. Inverse Realized Laplace Transforms for Nonparametric Volatility Density Estimation in Jump-Diffusions. Journal of American Statistical Association. 107: 622-635.
and . 2012. Realized Laplace Transforms for Pure-Jump Semimartingales. Annals of Statistics. 40(2): 1233-1262.
and . 2012. The Realized Laplace Transform of Volatility. Econometrica. 80: 1105-1127.
and . 2011. Estimation of Jump Tails. Econometrica. 79(6): 1727-1783.
and . 2011. Tails, Fears and Risk Premia. Journal of Finance. 66(6): 2165-2211.
,  and . 2011. Realized Laplace Transforms for Estimation of Jump Diffusive Volatility Models. Journal of Econometrics. 164(2): 367-381.
and . 2011. Volatility Jumps. Journal of Business and Economic Statistics. 29(3): 356-371.
and . 2011. Limit Theorems for Power Variations of Pure-Jump Processes with Application to Activity Estimation. Annals of Applied Probability. 21(2): 546-588.
. 2011. Econometric Analysis of Jump-Driven Stochastic Volatility Models. Journal of Econometrics. 160(1): 12-21.
and . 2010. Do Price and Volatility Jump Together?. Annals of Applied Probability. 20(4): 1425-1469.
and . 2010. Jumps and Betas: A New Theoretical Framework for Disentangling and Estimating Systematic Risks. Journal of Econometrics. 157: 220-235.
. 2010. Variance Risk Premium Dynamics: The Role of Jumps. Review of Financial Studies. 23(1): 345-383.
and . 2010. Activity Signature Functions for High-Frequency Data Analysis. Journal of Econometrics. 154: 125-138.
and . 2009. Testing for Common Arrival of Jumps in Discretely-Observed Multidimensional Processes. Annals of Statistics. 37: 1792-1838.
. 2009. Estimation of Continuous-Time Stochastic Volatility Models with Jumps Using High-Frequency Data. Journal of Econometrics. 148: 131-148.
and . 2006. Simulation Methods for Levy-driven CARMA Stochastic Volatility Models. Journal of Business & Economic Statistics. 24(4): 455-469.
. 2013. Power Variations from Second Order Differences foe Pure Jump Semimartingales. Stochastic Processes and Their Applications. 123: 2829-2850.
Working Papers
, ,  and . 2017. Unified Inference for Nonlinear Factor Models from Panels with Fixed and Large Time Span.
,  and . 2017. The Pricing of Tail Risk and the Equity Premium: Evidence from International Optional Markets.
, ,  and . 2017. Rank Tests at Jump Events.
Book Chapters
and . 2015. "Efficient Estimation of Integrated Volatility in Presence of Infinite Variation Jumps with Multiple Activity Indices." Springer-Verlag.
and . 2010. "Realized Volatility and Multipower Variation." In Encyclopedia of Quantitative Finance, edited by Ole Barndorff-Nielsen and Eric Renault, Wiley.

Print Teaching
Teaching Interests
Derivatives, investments
Full-Time / Evening & Weekend MBA
Derivative Markets I (FINC-465-0)
This course covers the use and pricing of forwards and futures, swaps and options. Specific topics include strategies for speculation and risk management, no-arbitrage pricing for forward contracts, the binomial and Black-Scholes option pricing models and applications of pricing models in other contexts.

Asset Pricing III (FINC-585-3)
This course covers topics in the empirical asset pricing literature with an emphasis on recent developments.  Topics covered include the following. GMM:  Theory and applications in empirical evaluation of asset pricing models; Stock index  return predictability based on past prices, dividends, earnings, and interest rates; Stock return predictability based on information in the large cross section of past stock prices and relative price momentum in stock prices; Portfolio performance evaluation; Specification and estimation of volatility models from low frequency data; Nonparametric measurement of volatility and jump risks from high frequency data; Empirical derivatives pricing using parametric and nonparametric  methods and applications to estimating no-arbitrage and structural models.

Derivatives (KELLG_FE-314-0)

Time Series Analysis (FINC-520-1)
The specification, estimation, and diagnostic testing of dynamic models involving economic time series present a host of unique statistical problems requiring the use of specialized inference procedures. This course provides an overview of some of the most important of these procedures. The focus will be on results most relevant for practical applications rather than formal proofs of theorems, with the various econometric techniques illustrated through problems in both macroeconomics and asset pricing finance.